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Montoya, Amanda K.; Edwards, Michael C. – Educational and Psychological Measurement, 2021

Model fit indices are being increasingly recommended and used to select the number of factors in an exploratory factor analysis. Growing evidence suggests that the recommended cutoff values for common model fit indices are not appropriate for use in an exploratory factor analysis context. A particularly prominent problem in scale evaluation is the…

Descriptors: Goodness of Fit, Factor Analysis, Cutting Scores, Correlation

Levy, Roy; Xia, Yan; Green, Samuel B. – Educational and Psychological Measurement, 2021

A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and…

Descriptors: Bayesian Statistics, Statistical Analysis, Factor Structure, Probability

Raborn, Anthony W.; Leite, Walter L.; Marcoulides, Katerina M. – Educational and Psychological Measurement, 2020

This study compares automated methods to develop short forms of psychometric scales. Obtaining a short form that has both adequate internal structure and strong validity with respect to relationships with other variables is difficult with traditional methods of short-form development. Metaheuristic algorithms can select items for short forms while…

Descriptors: Test Construction, Automation, Heuristics, Mathematics

Beauducel, André; Kersting, Martin – Educational and Psychological Measurement, 2020

We investigated by means of a simulation study how well methods for factor rotation can identify a two-facet simple structure. Samples were generated from orthogonal and oblique two-facet population factor models with 4 (2 factors per facet) to 12 factors (6 factors per facet). Samples drawn from orthogonal populations were submitted to factor…

Descriptors: Factor Structure, Factor Analysis, Sample Size, Intelligence

Fujimoto, Ken A.; Neugebauer, Sabina R. – Educational and Psychological Measurement, 2020

Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are…

Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Factor Structure

Liang, Xinya – Educational and Psychological Measurement, 2020

Bayesian structural equation modeling (BSEM) is a flexible tool for the exploration and estimation of sparse factor loading structures; that is, most cross-loading entries are zero and only a few important cross-loadings are nonzero. The current investigation was focused on the BSEM with small-variance normal distribution priors (BSEM-N) for both…

Descriptors: Factor Structure, Bayesian Statistics, Structural Equation Models, Goodness of Fit

Yang, Yanyun; Xia, Yan – Educational and Psychological Measurement, 2019

When item scores are ordered categorical, categorical omega can be computed based on the parameter estimates from a factor analysis model using frequentist estimators such as diagonally weighted least squares. When the sample size is relatively small and thresholds are different across items, using diagonally weighted least squares can yield a…

Descriptors: Scores, Sample Size, Bayesian Statistics, Item Analysis

Jordan, Pascal; Spiess, Martin – Educational and Psychological Measurement, 2019

Factor loadings and item discrimination parameters play a key role in scale construction. A multitude of heuristics regarding their interpretation are hardwired into practice--for example, neglecting low loadings and assigning items to exactly one scale. We challenge the common sense interpretation of these parameters by providing counterexamples…

Descriptors: Test Construction, Test Items, Item Response Theory, Factor Structure

Fujimoto, Ken A. – Educational and Psychological Measurement, 2019

Advancements in item response theory (IRT) have led to models for dual dependence, which control for cluster and method effects during a psychometric analysis. Currently, however, this class of models does not include one that controls for when the method effects stem from two method sources in which one source functions differently across the…

Descriptors: Bayesian Statistics, Item Response Theory, Psychometrics, Models

Gonzalez, Oscar; MacKinnon, David P. – Educational and Psychological Measurement, 2018

Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to…

Descriptors: Statistical Analysis, Monte Carlo Methods, Measurement, Models

Raykov, Tenko; Goldammer, Philippe; Marcoulides, George A.; Li, Tatyana; Menold, Natalja – Educational and Psychological Measurement, 2018

A readily applicable procedure is discussed that allows evaluation of the discrepancy between the popular coefficient alpha and the reliability coefficient of a scale with second-order factorial structure that is frequently of relevance in empirical educational and psychological research. The approach is developed within the framework of the…

Descriptors: Test Reliability, Factor Structure, Statistical Analysis, Computation

Zhang, Xijuan; Savalei, Victoria – Educational and Psychological Measurement, 2016

Many psychological scales written in the Likert format include reverse worded (RW) items in order to control acquiescence bias. However, studies have shown that RW items often contaminate the factor structure of the scale by creating one or more method factors. The present study examines an alternative scale format, called the Expanded format,…

Descriptors: Factor Structure, Psychological Testing, Alternative Assessment, Test Items

Hayduk, Leslie – Educational and Psychological Measurement, 2014

Researchers using factor analysis tend to dismiss the significant ill fit of factor models by presuming that if their factor model is close-to-fitting, it is probably close to being properly causally specified. Close fit may indeed result from a model being close to properly causally specified, but close-fitting factor models can also be seriously…

Descriptors: Factor Analysis, Goodness of Fit, Factor Structure, Structural Equation Models

Lee, HwaYoung; Beretvas, S. Natasha – Educational and Psychological Measurement, 2014

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

Descriptors: Item Analysis, Factor Structure, Bayesian Statistics, Goodness of Fit

Wiley, Edward W.; Shavelson, Richard J.; Kurpius, Amy A. – Educational and Psychological Measurement, 2014

The name "SAT" has become synonymous with college admissions testing; it has been dubbed "the gold standard." Numerous studies on its reliability and predictive validity show that the SAT predicts college performance beyond high school grade point average. Surprisingly, studies of the factorial structure of the current version…

Descriptors: College Readiness, College Admission, College Entrance Examinations, Factor Analysis